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A Convnet for Non-maximum Suppression

  • Jan HosangEmail author
  • Rodrigo Benenson
  • Bernt Schiele
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

Abstract

Non-maximum suppression (NMS) is used in virtually all state-of-the-art object detection pipelines. While essential object detection ingredients such as features, classifiers, and proposal methods have been extensively researched surprisingly little work has aimed to systematically address NMS. The de-facto standard for NMS is based on greedy clustering with a fixed distance threshold, which forces to trade-off recall versus precision. We propose a convnet designed to perform NMS of a given set of detections. We report experiments on a synthetic setup, crowded pedestrian scenes, and for general person detection. Our approach overcomes the intrinsic limitations of greedy NMS, obtaining better recall and precision.

Keywords

Detection Score Aspect Ratio Variance Person Detection Input Grid Object Proposal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

419026_1_En_16_MOESM1_ESM.pdf (17.9 mb)
Supplementary material 1 (pdf 18377 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Max-Planck Institute for InformaticsSaarbrückenGermany

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